Explainable AI: Advances in Interpretability Algorithms and Applications

Dear Colleagues,

The growing integration of artificial intelligence (AI) in critical domains such as healthcare, finance, law, and autonomous systems has intensified the need for models that are not only accurate, but also interpretable and transparent. In response, explainable artificial intelligence (XAI) has emerged as a field dedicated to opening the “black box” of complex AI systems, enabling human users to understand, trust, and effectively govern intelligent algorithms.

This Special Issue will focus on recent advances in explainability algorithms—both model-specific and model-agnostic—that aim to improve the transparency, accountability, and robustness of AI systems across diverse applications. As AI continues to be deployed in high-stakes environments, the development of reliable interpretability tools becomes essential for ensuring ethical use, regulatory compliance, and human-centric design.

We invite high-quality submissions that address the theoretical foundations, algorithmic innovations, and real-world implementations of explainable AI. We particularly welcome interdisciplinary research that bridges technical development with societal, legal, or ethical considerations.

Topics of interest include, but are not limited to, the following:

• Novel algorithms for local and global interpretability;
• Comparative studies of XAI methods (e.g., SHAP, LIME, Integrated Gradients, Anchors);
• Benchmarks, metrics, and evaluation frameworks for XAI;
• Explainability in ensemble learning, deep learning, and generative models;
• Interpretable AI in healthcare, finance, law, and scientific discovery;
• Human-in-the-loop and interactive explanations;
• Visualization techniques for explainable AI;
• The role of explainability in AI ethics, fairness, and accountability;
• Regulatory perspectives and standards for transparent AI;
• Robustness and reliability of explanation methods under adversarial conditions;
• Causal inference and counterfactual reasoning in XAI;
• Usability and cognitive dimensions of model explanations;
• Explainability in edge computing, IoT, and real-time systems.

We welcome original research articles, surveys, case studies, and critical reviews that contribute to advancing the field of interpretable and explainable artificial intelligence.

Dr. Antonio Sarasa-Cabezuelo
Guest Editor

Manuscript Submission Information

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